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In this paper we propose alternative estimators to ratio estimators given by Kadilar and Cingi (2006) and a class of estimators for population variance using the same approach adopted by Srivenkataramana (1980). Conditions under which the proposed estimators are more efficient than usual unbiased estimator, Isaki (1983) estimator, Biradar and Singh (1994) estimator and the estimators given by Kadilar and Cingi (2006) are obtained. The exact expressions for biases and mean squared errors of the proposed estimators are obtained. An empirical study has been carried out to demonstrate the performance of the suggested estimators. Also following Searls (1964), generalized version of the proposed class of estimators is also studied with its properties. [ABSTRACT FROM AUTHOR]

In this paper, we study the partially linear model, y=Xβ+f+ε. We introduce a new Liu-type estimator in a partially linear model, then we compared the new estimator with the two-step estimator in the mean squared error sense. Finally, we give a simulation study to explain the validity and feasibility of the approach. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we propose an improved estimator of finite population mean using information on two auxiliary attributes under simple random sampling (SRS) and two-phase sampling schemes. The bias and mean squared error (MSE) expressions of the proposed estimators are obtained up to first order of approximation. It is shown theoretically, that the proposed estimator is more efficient than the ratio-product type estimator, regression estimator, difference type estimator and Malik and Singh (2013) [13] estimator in both SRS and two phase sampling. An empirical study using two data sets is also conducted to support the theoretical findings. [Copyright &y& Elsevier]

Abstract: Recently, Koyuncu (2012) [3] proposed an efficient family of estimators for estimation of finite population mean using information on the auxiliary attribute. In this paper, we propose two improved estimators of finite population mean. The biases and mean squared errors of the proposed estimators are derived up to the first order of approximation. It is observed that the first proposed estimator is always better than the first family of estimators adapted by Koyuncu (2012) [3]. An empirical study is carried out to demonstrate the performance of the proposed estimators. [Copyright &y& Elsevier]

Abstract: In this paper we propose a class of chain ratio-type exponential estimators for population mean in double sampling using two auxiliary variates. The properties of the proposed class of estimators are studied under large sample approximation. It has been shown that the proposed class of estimators is more efficient than the ratio estimator in double sampling , Chand’s (1975) chain-type ratio estimator and Singh and Vishwakarma (2007) exponential ratio estimator. An empirical study is carried out in support of the present study. [Copyright &y& Elsevier]

Abstract: We propose a two parameter ratio-product-ratio estimator for estimating a finite population variance based on simple random sampling without replacement. The bias and mean square error of the proposed estimator are obtained to the first degree of approximation. We have derived the conditions for the parameters under which the proposed estimator has smaller mean square error than the sample variance, ratio and product estimators. We carry out an application showing that the proposed estimator outperforms the traditional estimators using different data sets. [Copyright &y& Elsevier]

Abstract: This paper proposes two product type exponential estimators of population mean of study variable using linear transformation of auxiliary variable with the assumption that the extreme values (i.e. maximum and minimum values) of auxiliary variable in the population are known in advance. The expressions for biases and mean square errors of proposed estimators are obtained, up to first order of approximation. Theoretical conditions have been made under which our proposed estimators are superior to the existing ones. The overall preference regions of all the estimators considered in this paper are constructed and represented them diagrammatically. A numerical illustration is considered to support and verify the superiority of our proposed estimators. [Copyright &y& Elsevier]

Abstract: In this paper, we propose the weighted moments estimators (WMEs) of the scale parameter of a Pareto distribution with known shape parameter under the type II multiply censored sample. Moreover, we give a computational comparison for these proposed WMEs and best linear unbiased estimator (BLUE) of the scale parameter on the basis of the exact mean squared error (MSE) for given sample sizes and different censoring schemes. Further, we also obtain the best estimator among the WMEs and BLUE. [Copyright &y& Elsevier]

Abstract: In this paper, we suggest the maximum likelihood estimator (MLE) of the quadratic function p 2 + βp (for different values of β) of the Bernoulli parameter, that is discussed for the inverse sampling (IS) and fixed size sampling (FSS) methods. Moreover, the IS method is compared with FSS method based on mean squared error (MSE). In consequence of MSE for MLE of the quadratic function p 2 + βp in two sampling methods are complicated, so we also give a computational comparison of MSE for p 2 + βp to assess the performance of two sampling methods by using numerical method. [Copyright &y& Elsevier]

The mean of the usual balanced ranked set sample (BRSS), as introduced by McIntyre [Aust. J. Agric. Res. 3 (1952) 385], is more efficient as an estimator of the population mean than the mean of a simple random sample (SRS). Hence in this regard, BRSS is considered as a non-parametric procedure. However, for estimating other parameters of the population, the BRSS is not as universal (non-parametric) as in the case of the mean of the population. In this paper, it is shown that, under some suitable conditions, any reasonable estimator of any parameter of a population based on a SRS, can be always dominated by the corresponding estimator using some RSS plans. [Copyright &y& Elsevier]